Data sparsity and cold-start problems are persistent challenges in recommendation systems. Cross-domain recommendation (CDR) is a promising solution that utilizes knowledge from the source domain to improve the recommendation performance in the target domain. Previous CDR approaches have mainly followed the Embedding and Mapping (EMCDR) framework, which involves learning a mapping function to facilitate knowledge transfer. However, these approaches necessitate re-engineering and re-training the network structure to incorporate transferrable knowledge, which can be computationally expensive and may result in catastrophic forgetting of the original knowledge. In this paper, we present a scalable and efficient paradigm to address data sparsity and cold-start issues in CDR, named CDR-Adapter, by decoupling the original recommendation model from the mapping function, without requiring re-engineering the network structure. Specifically, CDR-Adapter is a novel plug-and-play module that employs adapter modules to align feature representations, allowing for flexible knowledge transfer across different domains and efficient fine-tuning with minimal training costs. We conducted extensive experiments on the benchmark dataset, which demonstrated the effectiveness of our approach over several state-of-the-art CDR approaches.

翻译：数据稀疏性和冷启动问题是推荐系统中长期存在的挑战。跨域推荐（CDR）是一种有前景的解决方案，它利用源域知识提升目标域的推荐性能。以往的CDR方法主要遵循嵌入与映射（EMCDR）框架，通过学习映射函数来促进知识迁移。然而，这些方法需要重新设计和训练网络结构以融入可迁移知识，这不仅计算开销大，还可能导致原始知识的灾难性遗忘。本文提出一种可扩展且高效的范式——CDR-Adapter，通过将原始推荐模型与映射函数解耦，无需重新设计网络结构即可解决CDR中的数据稀疏性和冷启动问题。具体而言，CDR-Adapter是一种新颖的即插即用模块，利用适配器模块对齐特征表示，从而在不同域间实现灵活的知识迁移，并以极低的训练成本进行高效微调。我们在基准数据集上进行了大量实验，结果表明该方法在多个最先进的CDR方法中表现更优。